Handwriting Recognition Using Neural Networks

Click here to load reader

  • date post

    11-Mar-2015
  • Category

    Documents

  • view

    203
  • download

    3

Embed Size (px)

description

It will provides the details about Handwriting character recognition

Transcript of Handwriting Recognition Using Neural Networks

Handwriting Recognition using Neural Networks

KANTAIAH. CH Mtech(PTPG),SIT,JNTUH

Example Scanned Images

Handwriting Recognition Two different fields: Online Handwriting RecognitionWriter's pen movements captured Velocity, acceleration, stroke order etc. Style can be constrained (e.g. Graffitti gestures)

Offline Handwriting RecognitionOnly pixels Cannot constrain style (documents already written)

Offline is harder (less information) Genealogical records are all offline

Mary

Online Handwriting Recognition Modern systems are moderately successful, e.g. Microsoft Research's new Tablet PC:

Polynomial coefficients e.g. [0.94, 0.05, 0.29,...]

Offline Handwriting Recognition A difficult problem Almost as many approaches as there are researchers e.g. Pattern Recognition Statistical analysis Mathematical modelling Physics-based modelling Physics Subgraph matching / graph search Neural networks / machine learning Fractal image compression ... (too many to list) ...

Previous Work: Offline Online Conversion

Finding contour

Finding midline

Stroke ordering difficult problem

Offline Online Conversion ctd. Especially difficult with genealogical records:

Stroke ordering: difficult

Broken lines / blobs?

Not practical

Previous Work: Holistic Matching Whole word is stretched to match known words Sources of variation compound across word

Handwriting Recognition Some aspects of Handwriting Recognition:

Segmentation problem (can't read word until it is segmented; can't segment word until it is read) Different handwriting styles Use of dictionary to correct for errors in reading

nr? m?

Srnitb --> Smith

Thesis Approach: PreprocessingOutlines of word are traced and smoothed:

Handwriting slope is corrected for automatically:

Proposed System OverviewInputPre-processing (high curvature points) Segmentation

Dictionary

Character Recognizer

Recognition Engine

Context Models

Word or Number Candidates

Segmentation Goal: robustly cut letters into segments Match multiple segments to detect letters Easier than matching whole letter

Dynamic Global Search Assemble word spelling from possible letter readings

Best path: Williarw Suwkino (65% confidence)

Results (1)

Results (2)

Results (3)

Results (4)

In general: results even worse system only worked well on words it was specifically trained on

The Human Brain's Visual System

Retina

The Human Brain's Visual System

Angular edge detectors Retina

The Human Brain's Visual System

Line / curve detectors Angular edge detectors Retina

... ... ...

The Human Brain's Visual System

Feature detectors Line / curve detectors Angular edge detectors Retina ... ... ...

The Human Brain's Visual System

Lateral inhibition

Feature detectors Line / curve detectors Angular edge detectors Retina

Feedback

... ... ...

The Human Brain's Visual System

Letter / word shape recognizers Feature detectors Line / curve detectors Angular edge detectors Retina

JLateral inhibition Feedback

... ... ...

The Human Brain's Visual System

JosephLetter / word shape recognizers Feature detectors Line / curve detectors Angular edge detectors Retina

JLateral inhibition Feedback

... ... ...

Conclusions Handwriting recognition is important for genealogy... ...but it is hard Current methods don't work very well... ...and they don't operate much like the human brain Future work should focus on understanding the brain, and emulating it as much as possible, e.g. With: Hierarchical reasoning Feedback Lateral inhibition

Questions?

Thank You.